An Assessment of Self-Service Business Intelligence Tools for Students: The Impact of Cognitive Needs and Innovative Cognitive Styles

Introduction

Self-service has recently become a defining feature of modern business intelligence (BI) tools (Daradkeh & Al-Dwairi, 2017; Tavera Romero, Ortiz, Khalaf, & Ríos Prado, 2021). This wave of disruption has led to shifting the market and new buying trends away from IT-centric to business-centric BI platforms with self-service reporting and analytics capabilities (Alpar & Schulz, 2016). The goal of self-service BI is to empower business users to analyze, visualize, and glean insights from corporate data without the intervention of IT department (Imhoff & White, 2011). Self-service BI tools are characterized by intuitive and easy-to-use interfaces that support a full range of BI and analytics capabilities such as visualization, dashboards, OLAP multidimensional analysis, predictive analysis and statistical modeling (Daradkeh & Al-Dwairi, 2017). These capabilities focus on four main objectives: 1) providing easy access to source data for reporting and analysis; 2) building easy-to-use BI applications and improved support for data analysis; 3) providing fast-to-deploy data warehouse options such as cloud or mobile environments; and 4) designing intuitive, customizable and collaborative end-user interfaces (Imhoff & White, 2011; Salisu, Bin Mohd Sappri, & Bin Omar, 2021).

As BI and analytics vendors such as Tableau, Microsoft, MicroStrategy, Sisense, and QlikView are promoting different self-service BI tools in the market, they are interested in how to attract more consumers and organizations to adopt and buy their products (Gartner, 2019). Often, end users need to carefully evaluate different self-service BI tools before making adoption decisions (Daradkeh & Al-Dwairi, 2017). Since these tools often have similar features and can support the same reporting and analytical tasks, end users often face a major challenge in choosing a tool that best meets their business needs. Previous research has made a great contribution towards maturing and advancing technical capabilities of self-service BI tools (Abelló et al., 2013; Acito & Khatri, 2014; Alpar & Schulz, 2016; Bani-Hani, Pareigis, Tona, & Carlsson, 2018; Eckerson, 2014; Schlesinger & Rahman, 2016). However, there is limited understanding of how students evaluate different self-service BI tools before making a decision about their use, and how they determine which BI tool is appropriate for learning data analytics and business intelligence skills from an end-user perspective.

The main purpose of this chapter is to understand how student characteristics influence the evaluation of different self-service BI tools. Among the many student characteristics that can have a significant impact on the evaluation of self-service BI tools, this study focuses on two important characteristics: need for cognition (NFC) (Cacioppo & Petty, 1982) and innovative cognitive style (ICS) (Bagozzi & Foxall, 1996; Stum, 2009). NFC refers to the level of students’ elaboration and measures the cognitive effort that they are willing to exert in working on solving a problem or completing a task (Bhattacherjee & Sanford, 2006). Students with higher level of NFC are more inclined and motivated to exert greater effort to engage with technology evaluation (Xuequn & Yanjun, 2015). Therefore, NFC could have a significant impact on the evaluation of self-service BI tools. ICS, on the other hand, has been recognized as an important personality trait that can play a key role in the evaluation and adoption of new technologies (Xuequn & Yanjun, 2015). students with higher innovativeness are more flexible in trying new and different technologies. They also are more willing to break conventional paradigms of solving problems and more adaptable to the changes in the environment. Consequently, students with a higher innovativeness may learn creative features and innovative ways to perform a specific task; leading to a positive evaluation of self-service BI tools.